Abstract

Movement is a universal response to music, with dance often taking place in social settings. Although previous work has suggested that socially relevant information, such as personality and gender, are encoded in dance movement, the generalizability of previous work is limited. The current study aims to decode dancers’ gender, personality traits, and music preference from music‑induced movements. We propose a method that predicts such individual difference from free dance movements, and demonstrate the robustness of the proposed method by using two data sets collected using different musical stimuli. In addition, we introduce a novel measure to explore the relative importance of different joints in predicting individual differences. Results demonstrated near perfect classification of gender, and notably high prediction of personality and music preferences. Furthermore, learned models demonstrated generalizability across datasets highlighting the importance of certain joints in intrinsic movement patterns specific to individual differences. Results further support theories of embodied music cognition and the role of bodily movement in musical experiences by demonstrating the influence of gender, personality, and music preferences on embodied responses to heard music.

Abstract

Reading, much like music listening, is an immersive experience that transports readers while taking them on an emotional journey. Listening to complementary music has the potential to amplify the reading experience, especially when the music is stylistically cohesive and emotionally relevant. In this paper, we propose the first fully automatic method to build a dense soundtrack for books, which can play high-quality instrumental music for the entirety of the reading duration. Our work employs a unique text processing and music weaving pipeline that determines the context and emotional composition of scenes in a chapter. This allows our method to identify and play relevant excerpts from the soundtrack of the book’s movie adaptation. By relying on the movie composer’s craftsmanship, our book soundtracks include expert-made motifs and other scene-specific musical characteristics. We validate the design decisions of our approach through a perceptual study. Our readers note that the book soundtrack greatly enhanced their reading experience, due to high immersiveness granted via uninterrupted and style-consistent music, and a heightened emotional state attained via high precision emotion and scene context recognition.

Abstract

Music sharing trends have been shown to change during times of socio-economic crises. Studies have also shown that music can act as a social surrogate, helping to significantly reduce loneliness by acting as an empathetic friend. We explored these phenomena through a novel study of online music sharing during the Covid-19 pandemic in India. We collected tweets from the popular social media platform Twitter during India’s first and second wave of the pandemic (n = 1,364). We examined the different ways in which music was able to accomplish the role of a social surrogate via analyzing tweet text using Natural Language Processing techniques. Additionally, we analyzed the emotional connotations of the music shared through the acoustic features and lyrical content and compared the results between pandemic and pre-pandemic times. It was observed that the role of music shifted to a more community focused function rather than tending to a more self-serving utility. Results demonstrated that people shared music during the Covid-19 pandemic which had lower valence and shared songs with topics that reflected turbulent times such as Hardship and Exclusion when compared to songs shared during pre-Covid times. The results are further discussed in the context of individualistic versus collectivistic cultures.

Abstract

Predicting Individual Differences from Brain Responses to Music using Functional Network Centrality

Abstract

Functional connectivity derived from functional MRI reflects the neuronal synchronization between brain regions anchored through an anatomical infrastructure of white matter pathways that can be computed from diffusion MRI. The inter-relation between the structure-function topography is of high interest in

Abstract

It is our pleasure to welcome you to the 8th Annual Conference on Cognitive Science (ACCS8) held online and organized by the Amrita Mind Brain Center, Amrita Vishwa Vidyapeetham (Amrita University), Kerala India from January 20-22, 2022. Cognitive Science is an interdisciplinary domain that challenges researchers and scientists to examine cognitive processes and understand how the mind works, functions, and behaves. Given the interdisciplinary nature of cognitive science, The Government of India and the Department of Science and Technology had formed the Association for Cognitive Science (ACS) in 2013. In the last seven years, the ACS has been organizing an Annual Conference of Cognitive Science (ACCS) at some of the top universities in India including IIT Kanpur, IIT Gandhinagar, IIT Guwahati, IISc Bangalore, BITS Pilani Goa and University of Hyderabad. In the 2022 event organized by Amrita Vishwa Vidyapeetham, ACCS8 had 700 participants of which several of them were researchers or PhD students. The 30 oral and 61 poster presentations allowed 3 tracks of talks and 3 tracks of poster jam sessions. 2022’s ACCS has been graced by a wonderful array of 6 keynote speakers from various regions across the world. Prof. Nandini Chatterjee Singh from UNESCO MGIEP (India) talked on Game-based courses for learning – results of an online digital game-based course to build social and emotional skills in adolescents. Prof. Claudia Gandini Wheeler-Kingshott of University College London (UK) addressed on Principles of quantitative magnetic resonance imaging and relevance to clinical applications. Prof. Kenji Doya of Okinawa Institute of Science and Technology (Japan) spoke on Neural Circuits for Mental Simulation. Prof. Egidio D’Angelo of University Pavia (Italy) addressed Bottom-up and top-down strategies for multiscale brain modelling. Prof. Ned Block of New York University (USA) spoke on an empirical argument that perception is non-conceptual. Prof. Bhavani Rao R of Amrita University (India) addressed on “Humane” Computer Interaction – The role of HCI in facilitating social and vocational empowerment.

Abstract

Music sharing trends have been shown to change during times of socio-economic crises. Studies have also shown that music can act as a social surrogate, helping to significantly reduce loneliness by acting as an empathetic friend. We explored these phenomena through a novel study of online music sharing during the Covid-19 pandemic in India. We collected tweets from the popular social media platform Twitter during India’s first and second wave of the pandemic (n = 1,364). We examined the different ways in which music was able to accomplish the role of a social surrogate via analyzing tweet text using Natural Language Processing techniques. Additionally, we analyzed the emotional connotations of the music shared through the acoustic features and lyrical content and compared the results between pandemic and pre-pandemic times. It was observed that the role of music shifted to a more community focused function rather than tending to a more self-serving utility. Results demonstrated that people shared music during the Covid-19 pandemic which had lower valence and shared songs with topics that reflected turbulent times such as Hardship and Exclusion when compared to songs shared during pre-Covid times. The results are further discussed in the context of individualistic versus collectivistic cultures.

Abstract

The task of identifying emotions from a given music track has been an active pursuit in the Music Information Retrieval (MIR) community for years. Music emotion recognition has typically relied on acoustic features, social tags, and other metadata to identify and classify music emotions. The role of lyrics in music emotion recognition remains under-appreciated in spite of several studies reporting superior performance of music emotion classifiers based on features extracted from lyrics. In this study, we use the transformer-based approach model using XLNet as the base architecture which, till date, has not been used to identify emotional connotations of music based on lyrics. Our proposed approach outperforms existing methods for multiple datasets. We used a robust methodology to enhance web-crawlers' accuracy for extracting lyrics. This study has important implications in improving applications involved in playlist generation of music based on emotions in addition to improving music recommendation systems.

Abstract

Music affects and in some cases reflects one’s emotional state. Key to this influence is lyrics and their meaning in conjunction with the acoustic properties of the track. Recent work has focused on analysing these acoustic properties and showing that individuals prone to depression primarily consume low valence and low energy music. However, no studies yet have explored lyrical content preferences in relation to online music consumptionofsuchindividuals. Inthecurrentstudy,weexamine lyrical simplicity, measured as the Compressibility and Absolute Information Content of the text, associated with preferencesofindividualsatriskfordepression. Usingthe six-month listening history of 541 Last.fm users, we compare lyrical simplicitytrendsforusersgroupedasbeingatrisk(At-Risk)ofdepression from those that are not (No-Risk). Our findings reveal that At-Risk individuals prefer songs with greater information content(lowerCompressibility)onaverage,especiallyforsongs characterised as Sad. Furthermore, we found that At-Risk individuals also have greater variability of Absolute Information Content across their listening history. We discuss the results in light of existing socio-psychological lab-based research on music habits associated with depression and their relevance to naturally occurring online music listening behaviour.

Abstract

—Music has the power to evoke intense emotional experiences and regulate the mood of an individual. With the advent of online streaming services, research in music recommendation services has seen tremendous progress. Modern methods leveraging the listening histories of users for session-based song recommendations have overlooked the significance of features extracted from lyrics and acoustic content. We address the task of song prediction through multiple modalities, including tags, lyrics, and acoustic content. In this paper, we propose a novel deep learning approach by refining Attentive Neural Networks using representations derived via a Transformer model for lyrics and Variational Autoencoder for acoustic features. Our model achieves significant improvement in performance over existing state-of-the-art models using lyrical and acoustic features alone. Furthermore, we conduct a study to investigate the impact of users’ psychological health on our model’s performance. Index Terms—Music Recommend